Discussion: Data Analysis Ideas
The inclusion of both quantitative and qualitative data should add to the rigor of your
study. Analyzing and integrating data can be time consuming, but it is often the fertile
ground from which you harvest the stories and meanings of your study. Pay close attention
to how you enter data into software, and keep linking your data analysis choices to your
questions and framework.
To prepare:
• Review relevant materials from your qualitative and quantitative courses. Think
about what you learned in relationship to data analyses.
• Review the Learning Resources for this module.
• Consider your work in Module 3 related to instrument selection and issues of
trustworthiness and validity.
• Reflect on the issues and problems you anticipate as you plan for, collect and
analyze data for the Assignment in this module. How might you respond to these
challenges with the support of your peers and the Instructor?
• Consider what data analysis strategy or strategies are the most appropriate for your
research questions and data.
By Day 3 of Week 8
Construct a 2- to 3-paragraph post that identifies the qualitative and quantitative
approaches you will use to analyze your data. Explain why you have chosen these
particular strategies and tests; and tell how you have addressed, or plan to address, any
potential for validity, reliability and trustworthiness issues that you have identified.
Learning Resources
Course Toolbox
Document: RSCH 8460 Final Project Guidelines (PDF)
Document: Research Design Alignment Grid (PDF)
Walden University. (2016). How do I find a measurement, test, survey, or instrument?
Retrieved from http://academicanswers.waldenu.edu/faq/72673
Walden University Office of Research and Doctoral Services. (n.d.). Institutional Review
Board for Ethical Standards in Research. Retrieved from
http://academicguides.waldenu.edu/researchcenter/orec
Walden University Office of Research and Doctoral Services. (n.d.) MMR Dissertation
Checklist. Retrieved from http://academicguides.waldenu. edu/researchcenter/osra/phd
Walden University Office of Research and Doctoral Services. (n.d.) Professional Doctorate
Capstone Checklist (depending upon your degree). Retrieved from
http://academicguides.waldenu.edu/researchcenter/osra/professionaldoctoral
Walden University Office of Research and Doctoral Services. (n.d.) Oral Defense Archives.
Retrieved from http://academicguides.waldenu.edu/researchcenter/osra/oraldefense
Walden University Office of Research and Doctoral Services. (n.d.). Research ethics &
compliance: Application & general materials. Retrieved from:
http://academicguides.waldenu.edu/researchcenter/orec/application#s-lg-box-2713767
Walden University Writing Center. (n.d.) Where can I find a literature review matrix?
Retrieved from: http://academicanswers.waldenu.edu/a.php?qid=683906
Walden University Library (Director). (2016, September 22). Finding mixed methods
studies [Video file]. Retrieved November 4, 2016. https://youtu.be/VmfCUoxQHdQ.
Note: The approximate length of this media piece is 2 minutes.
Walden University Library. (n.d.). Form and style checklists. Retrieved from:
http://academicguides.waldenu.edu/formandstyle/process/checklist
Required Readings
Plano Clark, V. L., & Ivankova, N. V. (2016). Mixed methods research: A guide to the field.
Thousand Oaks, CA: SAGE Publications.
• Chapter 5. “How to Use Mixed Methods Research? Understanding the Basic
Mixed Methods Designs”
This chapter discusses debates surrounding typologies and logic models used
in MMR. They identified three basic MM designs.
Bazeley, P. (2012). Integrative analysis strategies for mixed data sources. American
Behavioral Scientist, 56(6), 814-828. doi:10.1177/0002764211426330
Retrieved from the Walden Library databases.
The author discusses integrative mixed methods analysis strategies using five distinctions.
Review the examples and consider how your integration plans fit into one of the categories.
Bazeley, P., & Kemp, L. (2012). Mosaics, triangles, and DNA: Metaphors for integrated
analysis in mixed methods research. Journal of Mixed Methods Research, 6(1), 55–72. doi:
10.1177/1558689811419514
Retrieved from the Walden Library databases.
This article explores how metaphors have been used to describe integration practices in
MMR. It distills eight principles of integration.
Optional Resources
Walden Resources
Walden University. (n.d.). Doctoral capstone document templates. Retrieved from
https://academicguides.waldenu.edu/formandstyle/programs#s-lg-box-11273110
Walden University Library. (n.d.). Form and style checklists. Retrieved from
http://academicguides.waldenu.edu/formandstyle/process/checklist
Walden University Library. (n.d.). PhD dissertation resources. Retrieved from
http://academicguides.waldenu.edu/writingcenter/capstone/phd
Walden University Library. (n.d.) Research ethics planning worksheet. Retrieved from
http://academicguides.waldenu.edu/ld.php?content_id=16791936
Walden University Library. (n.d.). Tests and measures. Retrieved from
http://academicguides.waldenu.edu/library/testsmeasures
Walden University Office of Student Research Administration. (n.d.) PhD dissertation
program. Retrieved from http://academicguides.waldenu.edu/researchcenter/osra/phd
Here are my class mate discussion. That might help you . please note my
interest is different. Diabetic social disparities poor management of the
disease due to poor socioeconomic
Olbami
OLUWATAYO ADULOJU WEEK 8 DISCUSSION: DATA ANALYSIS IDEAS – MODULE
4
COLLAPSE
The Explanatory Sequential Mixed Methods Research Design for the study of Acute and Chronic
Multidimensional Poverty will deploy the following data analysis strategies and approaches:
Quantitative Strand: First, conduct quantitative analysis to derive the Chronic Multidimensional Poverty
Index, using secondary source global MPI databases (this is archival data from 2015-2022) – this will provide
the basis for triangulating where in Nigeria, chronic and acute poverty has the highest incidence and intensity
at the Level of States, it will also allow us to see the structure and composition of overlapping deprivations.
Second, conduct a Cluster analysis of national MPI databases in 2022 to derive the Acute Multidimensional
Poverty Index. Third, conduct a Correlation Analysis of the Relationship between Chronic and Acute
Multidimensional Poverty Index and Identify areas with the highest combined incidence (that is, Nigeria
States with the highest intensity and incidence of multidimensional deprivations in the States with the
convergence of chronic and acute poverty). Threats and Mitigations: With the aggregation of a range of
indicators and dimensions in the AF method for selected dimensions of poverty, there are potential threats to
internal validity. Alkire and Foster (2011) prescribe the guidelines for selecting and adopting indicators,
setting thresholds, weights and dimensions – all of which need to meet internal validity tests of “dimensional
decomposability, monotonicity and symmetry.” The consistency of the databases and sample quality from
which the indicators, thresholds, weights and dimensions are evaluated provide a threat to validity if there
are not from the same datasets. Each dimensional candidate measure must meet robustness text for internal
validity: basic disaggregation, changes in poverty cut-off and statistical inference. Statistical tests for
candidate measures robustness include Rank correlation, pair-wise, and appropriate confidence levels across
all parameters.
Qualitative Strand: Fourth using a database of heads of Households from the National Security Register to
identify Heads of Households in the States with the highest incidence of the combined phenomenon, using
purposive sampling to select participants for interviews (heads of households across poor local
governments). An adapted interview instrument is used for the data collection to understand the breadth,
depth, complexity, sequence and composition of their experience. Data Analysis will consist of Structural and
Descriptive Coding, Visual Representation, with Thematical Analysis of Interview Data of the Head of
Households. Threats and Mitigations: To mitigate the risk of not meeting data saturation. A structural and
Descriptive Coding approach will be used for the open coding and first, second and third rounds for the
identification of codes, categories and themes. Each sample will be coded until it reaches the theoretical
saturation milestone or point in the data analysis where the collection of additional data for a specific
category of a phenomenon under observation does not in any way contribute more to the further
development of the properties of that category. This will be repeated in several locations where the combined
effects of acute and chronic multidimensional poverty are experienced. Guest et al. (2006) note that reaching
theoretical saturation increases the credibility and validity of qualitative data analysis. To reduce the
subjectivity of interpretation in an effort to mediate that at the analysis and coding level, multiple coding will
be used to achieve Interrater Reliability. Ravitch and Carl (2019) note that the process of multiple raters
collectively creating a set of codes and code definitions, after which each rater codes and analyses a specific,
agreed-upon number of transcripts data sources – creates the basis for code convergence and divergence,
based on where the data analysis do or do not overlap. The pilot of the adapted instrument will provide a
unique opportunity to test interrater realibility.
References
Aduloju, O. (2020). Doctoral Dissertation Prospectus Draft: Understanding Combined Acute and Chronic
Multidimensional Poverty in Nigeria using Sequential Explanatory Mixed Methods Design. Walden University.
Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of public
economics, 95(7-8), 476-487.
Guest, G., Bunce, A. & Johnson, L. (2006). How many interviews are enough? An experiment with data
saturation and variability. Field Methods 18(1), 59–82.
Ravitch, S. M.& Carl, N. M. (2019). Qualitative Research: Bridging the Conceptual, Theoretical, and
Methodological, 2nd Edition. [[VitalSource Bookshelf version]]. Retrieved from vbk://9781544333809
Kim
Mixed Methods Data Analysis
The plan for data analysis is an important component of the mixed methods approach and
overall research process. The integration of quantitative and qualitative data must occur at
various phases of the mixed methods research process (Plano Clark & Ivankova, 2016). In the
explanatory sequential design, the quantitative data will guide the qualitative phase of the data
collection and analysis which reflects connecting the data through integration at the methods
level (Munce & Onwuegbuzie, 2020). The research design decisions will guide the timing,
integration, and priority of the quantitative and qualitative portions to address specific research
purposes in a rigorous, sound approach (Plano Clark & Ivankova, 2016). The quantitative data
analysis will guide the qualitative data collection and analysis.
Quantitative Data and Analysis
Once the research question, hypothesis, and variables with levels of measurement are
decided the quantitative data is collected the analysis process may be initiated. Statistical
procedures are divided into descriptive statistics to organize and understand the quantitative data
collected for a sample or population and inferential statistics make a prediction or inference
about a population based on observations and analysis of a sample (Frankfort-Nachmias et al.,
2020). The descriptive statistics of the desired kinship care provider research will be utilized to
organize and understand the characteristics of kinship care providers, experiences of stress and
strain, and extraordinary environmental events. The data will be presented visually through
statistical graphic diagrams to communicate the details of the knowledge in an accessible manner
(Wagner, 2020) with flexibility to discover patterns such as trends, clusters, and outliers (Yuan et
al., 2021). Furthermore, a bivariate table will display the distribution of one variable across the
categories of another variable which will assist to relate if there is a relationship, the strength,
and direction (Frankfort-Nachmias et al., 2020). Based on the ability to make common statistical
assumptions the parametric comparison test, paired t-test, will be utilized to understand the
relationship between formal and informal kinship care providers while the correlation,
Pearson’s r, will be utilized to indicate if the variables are related without a cause-and-effect
relationship. The utilization of the research design and attempt to control the factors to impact
the independent variable effect on the dependent variable with correlational analysis will
increase the validity of the results (Frankfort-Nachmias et al., 2020). There will be particular
attention to ensure the validity and reliability of the quantitative measurement while the
credibility, confirmability, dependability, and transferability will represent the trustworthiness to
facilitate the qualitative data collection process (Plano Clark & Ivankova, 2016).
Qualitative Data and Analysis
The qualitative data collection of the explanatory sequential mixed methods design will
be based on the results of the quantitative portion. The qualitative data analysis will transform
and create sense of the data by the assignment of codes, identification of categories, and
emergence of themes that relate to and answer the research question (Ravitch & Carl, 2021). The
multistep analysis process will systematically reduce, reorganize, and represent the collective
data to form a new whole (Saldaña, 2016). Qualitative data analysis is interpretive (Childs et al.,
2018) and requires the ability to think elastically and creatively (Laureate Education, 2016).
Reliability will be facilitated through data and methodological consistency while validity will be
produced through an accurate reflection of the phenomenon studied (Stewart & Hitchcock,
2020). Credibility will be addressed through reflexivity, triangulation, peer review, and member
checking to ensure the results accurately reflect the data. The thick descriptions of the setting,
participants, and evidence to support the results will increase the transferability of the results.
However, at this point the qualitative data will be utilized to further explain the quantitative data
and results with inferences created. Overall, the research question will guide the design and
impact the analysis process to produce quality results to facilitate social change and create a
better world.
References
Childs, E. & Demers, L. B. (2018). Qualitative coding boot camp: An intensive training and overview
for clinicians, educators, and administrators. MedEdPortal, 14.
https://doi.org/10.15766/mep_2374-8265.10769
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse
society (9th ed.). Sage.
Laureate Education (Producer). (2016). First cycle coding: Structural coding [Video file]. Baltimore,
MD: Author
Munce, S. E. P., & Onwuegbuzie, A. J. (2020). Mixed methods analysis. In Burkholder, G. J., Cox, K.
A., Crawford, L. M., & Hitchcock, J. H. (Eds.). Research designs and methods: An applied
guide for the scholar-practitioner. (pp. 129–143). Sage.
Plano Clark. V. L., & Ivankova, N. V. (2016). Mixed methods research: A guide to the field. Sage.
Ravitch, S. M., & Carl, N. M. (2021). Qualitative research: Bridging the conceptual, theoretical, and
methodological (2nd ed.). Sage.
Stewart, M. S., & Hitchcock, J. H. (2020). Quality considerations. In Burkholder, G. J., Cox, K. A.,
Crawford, L. M., & Hitchcock, J. H. (Eds.). Research designs and methods: An applied guide
for the scholar-practitioner. (pp. 3–12). Sage.
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science
statistics (7th ed.). Sage.
Yaun, J., Xiang, S., Xia, J., Yu, L., & Lui, S. (2021). Evaluation of sampling methods for
scatterplots. Transactions on Visualization and Computer Graphics.27(2), 1720–1730.
https://doi.org/10.1109/TVCG.2020.3030432

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